Imperial College London
Alternate

ProfessorSalmanRawaf

Faculty of Medicine, School of Public Health

Director of WHO Collaborating Centre
 
 
 
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Contact

 

+44 (0)20 7594 8814s.rawaf

 
 
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Assistant

 

Ms Ela Augustyniak +44 (0)20 7594 8603

 
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Location

 

311Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yousif:2023:10.21123/bsj.2023.8875,
author = {Yousif, MG and Hashim, K and Rawaf, S},
doi = {10.21123/bsj.2023.8875},
journal = {Baghdad Science Journal},
pages = {1507--1519},
title = {Post COVID-19 effect on medical staff and doctors' productivity analysed by machine learning},
url = {http://dx.doi.org/10.21123/bsj.2023.8875},
volume = {20},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. These results reflect the overall impact observed following the entire course of the COVID-19 pandemic and are not specific to a particular wave. The analysis revealed that older participants experienced a more pronounced decline in productivity, with a mean decrease of 35% compared to younger participants. Female participants, on average, had a 28% decrease in productivity compared to their male counterparts. Moreover, individuals with lower socioeconomic status exhibited a substantial decline in productivity, experiencing an average decrease of 40% compared to those with higher socioeconomic status. Similarly, participants who slept for fewer hours per night had a significant decline in productivity, with an average decrease of 33% compared to those who had sufficient sleep. The machine learning analysis identified age, specialty, COVID-19 complications, socioeconomic status, and sleeping time as crucial predictors of productivity score. The study highlights the significant impact of post-COVID-19 on the productivity of medical staff and doctors in Iraq. The findings can aid healthcare organizations in devising strategies to mitigate the negative consequences of COVID-19 on medical staff and doctors' productivity.
AU  - Yousif,MG
AU  - Hashim,K
AU  - Rawaf,S
DO  - 10.21123/bsj.2023.8875
EP  - 1519
PY  - 2023///
SN  - 2078-8665
SP  - 1507
TI  - Post COVID-19 effect on medical staff and doctors' productivity analysed by machine learning
T2  - Baghdad Science Journal
UR  - http://dx.doi.org/10.21123/bsj.2023.8875
UR  - https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8875
UR  - http://hdl.handle.net/10044/1/106166
VL  - 20
ER  -
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